IES84961Y1 - Image processing method and apparatus - Google Patents
Image processing method and apparatusInfo
- Publication number
- IES84961Y1 IES84961Y1 IE2007/0518A IE20070518A IES84961Y1 IE S84961 Y1 IES84961 Y1 IE S84961Y1 IE 2007/0518 A IE2007/0518 A IE 2007/0518A IE 20070518 A IE20070518 A IE 20070518A IE S84961 Y1 IES84961 Y1 IE S84961Y1
- Authority
- IE
- Ireland
- Prior art keywords
- facial
- regions
- image
- region
- main image
- Prior art date
Links
- 238000003672 processing method Methods 0.000 title claims description 7
- 230000001815 facial Effects 0.000 claims abstract description 109
- 230000002950 deficient Effects 0.000 claims abstract description 26
- 230000000875 corresponding Effects 0.000 claims abstract description 18
- 238000000034 method Methods 0.000 abstract description 21
- 238000004458 analytical method Methods 0.000 description 21
- 210000001508 Eye Anatomy 0.000 description 18
- 210000000887 Face Anatomy 0.000 description 14
- 210000000214 Mouth Anatomy 0.000 description 11
- 210000003491 Skin Anatomy 0.000 description 7
- 230000014509 gene expression Effects 0.000 description 7
- 238000001514 detection method Methods 0.000 description 5
- 230000004397 blinking Effects 0.000 description 4
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- 239000000203 mixture Substances 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000002156 mixing Methods 0.000 description 3
- 210000003128 Head Anatomy 0.000 description 2
- 241000593989 Scardinius erythrophthalmus Species 0.000 description 2
- 206010048232 Yawning Diseases 0.000 description 2
- 238000010191 image analysis Methods 0.000 description 2
- 238000003702 image correction Methods 0.000 description 2
- 201000005111 ocular hyperemia Diseases 0.000 description 2
- 230000002093 peripheral Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 210000004709 Eyebrows Anatomy 0.000 description 1
- 210000000744 Eyelids Anatomy 0.000 description 1
- 210000001061 Forehead Anatomy 0.000 description 1
- 210000000554 Iris Anatomy 0.000 description 1
- 210000001331 Nose Anatomy 0.000 description 1
- 210000001747 Pupil Anatomy 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
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- 238000003708 edge detection Methods 0.000 description 1
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- 238000003384 imaging method Methods 0.000 description 1
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Classifications
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03B—APPARATUS OR ARRANGEMENTS FOR TAKING PHOTOGRAPHS OR FOR PROJECTING OR VIEWING THEM; APPARATUS OR ARRANGEMENTS EMPLOYING ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ACCESSORIES THEREFOR
- G03B19/00—Cameras
- G03B19/02—Still-picture cameras
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- G06K9/00288—
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- G06K9/6255—
Abstract
ABSTRACT An image processing technique includes acquiring a main image of a scene and determining one or more facial regions in the main image. The facial regions are analysed to determine if any of the facial regions includes a defect. A sequence of relatively low resolution images nominally of the same scene is also acquired. One or more sets of low resolution facial regions in the sequence of low resolution images are determined and analysed for defects. Defect free facial regions of a set are combined to provide a high quality defect free facial region. At least a portion of any defective facial regions of the main image are corrected with image information from a corresponding high quality defect firee facial region.
Description
IMAGE PROCESSING METHOD AND APPARATUS
BACKGROUND
The present invention relates to an image processing method and apparatus. One of the most
common reasons for an acquired digital photograph to be discarded or spoiled is because one or
more of the facial regions in the photograph suffer from photographic defects other than red-eye
defects, even though red eye defects can be common in cameras not operating with the
advantages of the techniques described, eg, at US patent 6,407,777, and at US published
applications nos. 2005/0140801, 2005/0041 l2 1, 2006/0093212, and 2006/0204054. Common
examples occur when people move or shake their head; when someone closes their eyes or blinks
or someone yawns. Where there are several faces in a photograph, it is sufficient for one face to
be “defective” for the whole shot to be spoiled. Although digital cameras allow users to quickly
shoot several pictures of the same scene. Typically, such cameras do not provide warnings of
facial errors, nor provide a way to correct for such errors without repeating the composition
stages (i.e. getting everyone together again in a group) of taking the photograph and re-shooting
the scene. This type of problem is particularly difficult with children who are often photographed
in unusual spontaneous poses, which cannot be duplicated. When such a shot is spoiled because
the child moved their head at the moment of acquisition, it is very disappointing for the
photographer.
US patent no. 6,301,440, discloses an image acquisition device wherein the instant of exposure is
controlled by image content. When a trigger is activated, the image proposed by the user is
analysed and imaging parameters are altered to obtain optimum image quality before the device
proceeds to take the image. For example, the device could postpone acquisition of the image
until every person in the image is smiling.
SUMMARY OF THE INVENTION
An image processing method is provided including acquiring a main image of a scene. One or
more facial regions are determined in the main image. The one or more main image facial
regions are analyzed for defects and one or more are determined to be defective. A sequence of
relatively low resolution images nominally of the scene are acquired. One or more sets of low
resolution facial regions in the sequence are analyzed to determine one or more that correspond
to a defective main image facial region. At least a portion of the defective main image facial
region is corrected with image information from one or more corresponding low resolution facial
regions not including a same defect as said portion of said defective main image facial region.
The sequence of low resolution images may be specifically acquired for a time period not
including a time for acquiring the main image. The method may also include combining defect-
free low resolution facial regions into a combined image, and correcting at least the portion of
the defective main image facial region with image information from the combined image.
Another image processing method is provided that including acquiring a main image ofa scene.
One or more facial regions in the main image are determined, and analyzed to determine if any
are defective. A sequence of relatively low resolution images is acquired nominally of the scene
for a time period not including a time for acquiring the main image. One or more sets of low
resolution facial regions are determined in the sequence of low resolution images. The sets of
facial regions are analyzed to determine if any facial regions of a set corresponding to a defective
facial region of the main image include a defect. Defect free facial regions of the corresponding
set are combined to provide a high quality defect free facial region. At least a portion of any
defective facial regions of said main image are corrected with image information from a
corresponding high quality defect free facial region.
The time period may include one or more of a time period preceding or a time period following
the time for acquiring the main image. The correcting may include applying a model including
multiple vertices defining a periphery of a facial region to each high quality defect-free facial
region and a corresponding defective facial region. Pixels may be mapped of the high quality
defect-free facial region to the defective facial region according to the correspondence of vertices
for the respective regions. The model may include an Active Appearance Model (AAM).
The main image may be acquired at an exposure level different to the exposure level of the low
resolution images. The correcting may include mapping luminance levels of the high quality
defect free facial region to luminance levels of the defective facial region.
Sets of low resolution facial regions from the sequence of low resolution images may be stored
in an image header file of the main image.
The method may include displaying the main image and/or corrected image, and selected actions
may be user-initiated.
The analyzing of the sets may include, prior to the combining in the second method, removing
facial regions including faces exceeding an average size of faces in a set of facial regions by a
threshold amount from said set of facial regions, and/or removing facial regions including faces
with an orientation outside an average orientation of faces in a set of facial regions by a threshold
amount from said set of facial regions.
The analyzing of sets may include the following:
applying an Active Appearance Model (AAM) to each face of a set of facial regions;
analyzing AAM parameters for each face of the set of facial regions to provide an
indication of facial expression; and
prior to the combining in the second method, removing faces having a defective
expression from the set of facial regions.
The analyzing of sets may include the following:
applying an Active Appearance Model (AAM) to each face of a set of facial regions;
analysing AAM parameters for each face of the set of facial regions to provide an
indication of facial orientation; and
prior to said combining in the second method, removing faces having an undesirable
orientation from said set of facial regions.
The analyzing of facial regions may include applying an Active Appearance Model (AAM) to
each facial region, and analyzing AAM parameters for each facial region to provide an indication
of facial expression, and/or analyzing each facial region for contrast, sharpness, texture,
luminance levels or skin color or combinations thereof, and/or analyzing each facial region to
determine if an eye of the facial region is closed, if a mouth of the facial region is open and/or if
a mouth of the facial region is smiling.
The method may be such that the correcting, and the combining in the second method, only
occur when the set of facial regions exceeds a given number. The method may also include
resizing and aligning faces of the set of facial regions, and the aligning may be performed
according to cardinal points of faces of the set of facial regions.
The correcting may include blending and/or infilling a corrected region of the main image with
the remainder of the main image.
BRIEF DESCRIPTION OF THE DRAWINGS
Embodiments will now be described, by way of example, with reference to the accompanying
drawings, in which:
Figure l is a block diagram of an image processing apparatus operating in accordance with an
embodiment of the present invention;
Figure 2 is a flow diagram of an image processing method according to a preferred embodiment
of the present invention; and
Figures 3 and 4 show exemplary sets of images to which an active appearance model has been
applied.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
Certain embodiments can be implemented with a digital camera which incorporates (i) a face
tracker operative on a preview image stream; (ii) a super-resolution processing module
configured to create a higher resolution image from a composite of several low-resolution
images; and (iii) a facial region quality analysis module for determining the quality of facial
regions.
Preferably, super-resolution is applied to preview facial regions extracted during face tracking.
The embodiments enable the correction of errors or flaws in the facial regions of an acquired
image within a digital camera using preview image data and employing super-resolution
techniques.
Figure l is a block diagram of an image acquisition device 20, which in the present embodiment
is a portable digital camera, operating in accordance with certain embodiments. It will be
appreciated that many of the processes implemented in the digital camera are implemented in or
controlled by software operating on a microprocessor, central processing unit, controller, digital
signal processor and/or an application specific integrated circuit, collectively depicted as
processor I20. All user interface and control of peripheral components such as buttons and
display is controlled by a microcontroller 122.
In operation, the processor 120, in response to a user input at I22, such as half pressing a shutter
button (pre-capture mode 32), initiates and controls the digital photographic process. Ambient
light exposure is determined using a light sensor 40 in order to automatically determine if a flash
is to be used. The distance to the subject is determined using a focusing mechanism 50 which
also focuses the image on an image capture device 60. If a flash is to be used, processor 120
causes a flash device 70 to generate a photographic flash in substantial coincidence with the
recording of the image by the image capture device 60 upon full depression of the shutter button.
The image capture device 60 digitally records the image in colour. The image capture device is
known to those familiar with the art and may include a CCD (charge coupled device) or CMOS
to facilitate digital recording. The flash may be selectively generated either in response to the
light sensor 40 or a manual input 72 from the user of the camera. The high resolution image
recorded by image capture device 60 is stored in an image store 80 which may comprise
computer memory such a dynamic random access memory or a non-volatile memory. The
camera is equipped with a display 100, such as an LCD, both for displaying preview images and
displaying a user interface for camera control software.
ln the case of preview images which are generated in the pre-capture mode 32 with the shutter
button half-pressed, the display 100 can assist the user in composing the image, as well as being
used to determine focusing and exposure. Temporary storage 82 is used to store one or plurality
of the stream of preview images and can be part of the image store 80 or a separate component.
The preview image is usually generated by the image capture device 60. For speed and memory
efficiency reasons, preview images usually have a lower pixel resolution than the main image
taken when the shutter button is fully depressed, and are generated by sub-sampling a raw
captured image using software 124 which can be part of the general processor 120 or dedicated
hardware or combination thereof.
In the present embodiment, a face detection and tracking module 130 such as described in Irish
Application No. S2006/0826, filed November l5, 2006is operably connected to the sub-sampler
124 to control the sub-sampled resolution of the preview images in accordance with the
requirements of the face detection and tracking module. Preview images stored in temporary
storage 82 are available to the module I30 which records the locations of faces tracked and
detected in the preview image stream. In one embodiment, the module 130 is operably connected
to the display 100 so that boundaries of detected and tracked face regions can be superimposed
on the display around the faces during preview.
In the embodiment of Figure 1, the face tracking module 130 is arranged to extract and store
tracked facial regions at relatively low resolution in a memory buffer such as memory 82 and
possibly for storage as meta-data in an acquired image header stored in memory 80. Where
multiple face regions are tracked, a buffer is established for each tracked face region. These
buffers are of finite size (10-20 extracted face regions in a preferred embodiment) and generally
operate on a first-in-first-out (FIFO) basis.
According to the preferred embodiment, the device 20 further comprises an image correction
module 90. Where the module 90 is arranged for off-line correction of acquired images in an
external processing device 10, such as a desktop computer, a colour printer or a photo kiosk, face
regions detected and/or tracked in preview images are preferably stored as meta-data within the
image header. However, where the module 90 is implemented within the camera 20, it can have
direct access to the buffer 82 where preview images and/or face region information is stored.
In this embodiment, the module 90 receives the captured high resolution digital image from the
store 80 and analyzes it to detect defects. The analysis is performed as described in the
embodiments to follow. If defects are found, the module can modify the image to remove the
defect. The modified image may be either displayed on image display 100, saved on a persistent
storage 112 which can be internal or a removable storage such as CF card, SD card or the like, or
downloaded to another device via image output means 1 10 which can be tethered or wireless.
The module 90 can be brought into operation either automatically each time an image is
captured, or upon user demand via input 30. Although illustrated as a separate item, where the
module 90 is part of the camera, it may be implemented by suitable software on the processor
120.
The main components of the image correction module include a quality module 140 which is
arranged to analyse face regions from either low or high resolution images to determine if these
include face defects. A super-resolution module 160 is arranged to combine multiple low-
resolution face regions of the same subject generally with the same pose and a desirable facial
expression to provide a high quality face region for use in the correction process. In the present
embodiment, an active appearance model (AAM) module 150 produces AAM parameters for
face regions again from either low or high resolution images.
AAM modules are well known and a suitable module for the present embodiment is disclosed in
“Fast and Reliable Active Appearance Model Search for 3-D Face Tracking”, F Domaika and J
Ahlberg, IEEE Transactions on Systems, Man, and Cybemetics-Part B: Cybernetics, Vol. 34,
N0. 4, pg 1838-1853, August 2004, although other models based on the original paper by TF
Cootes et al “Active Appearance Models” Proc. European Conf. Computer Vision, 1998, pp 484-
498 could also be employed.
The AAM module 150 can preferably cooperate with the quality module 140 to provide pose
and/or expression indicators to allow for selection of images in the analysis and optionally in the
correction process described below. Also, the AAM module 150 can preferably cooperate with
the super-resolution module 160 to provide pose indicators to allow for selection of images in the
correction process, again described in more detail below.
Referring now to Figure 2, which illustrates an exemplary processing flow for certain
embodiments, when a main image is acquired, step 230, the location and size of any
detected/tracked face region(s) in the main acquired image (high resolution) will be known by
the module 90 from the module 130. Face detection can either be applied directly on the
acquired image and/or information for face regions previously detected and/or tracked in the
preview stream can be used for face detection in the main image (indicated by the dashed line
extending from step 220). At step 250, the facial region quality analysis module 140 extracts and
analyzes face regions tracked/detected at step 240 in the main image to determine the quality of
the acquired face regions. For example, the module 140 can apply a preliminary analysis to
measure the overall contrast, sharpness and/or texture of detected face region(s). This can
indicate if the entire face region was blurred due to motion of the subject at the instant of
acquisition. If a facial region is not sufficiently well defined then it is marked as a blur defect. In
additional or alternatively, another stage of analysis can focus on the eye region of the face(s) to
determine if one, or both eyes were fully or partially closed at the instant of acquisition and the
face region is categorized accordingly. As mentioned previously, if AAM analysis is performed
on the image, then the AAM parameters can be used to indicate whether a subject’s eyes are
open or not. It should be noted that in the above analyses, the module 90 detects blink or blur due
to localized movement of the subject as opposed to global image blur.
Another or alternative stage of analysis focuses on the mouth region and determines if the mouth
is opened in a yawn or indeed not smiling; again the face region is categorized accordingly. As
mentioned previously, if AAM analysis is performed on the image, then the AAM parameters
can be used to indicate the state of a subject’s mouth.
Other exemplary tests might include luminance levels, skin colour and texture histograms, abrupt
facial expressions (smiling, frowning) which may cause significant variations in facial features
(mouth shape, furrows in brow). Specialized tests can be implemented as additional or
alternative image analysis filters, for example, a Hough transfonn filter could be used to detect
parallel lines in a face region above the eyes indicating a “furrowed brow”. Other image analysis
techniques such as those known in the art and as disclosed in US 6,301,440 can also be
employed to categorise the face region(s) of the main image.
After this analysis, it is decided (for each face region) if any of these defects occurred, step 260,
and the camera or external processing device user can be offered the option of repairing the
defect based on the buffered (low resolution) face region data, step 265.
When the repair option is actuated by the user, each of the low-resolution face regions is first
analyzed by the face region quality analyzer, step 270. As this analysis is operative on lower
resolution images acquired and stored at steps 200/210, the analysis may vary from the analysis
of face regions in the main acquired image at step 250. Nevertheless the analysis steps are
similar in that each low-resolution face region is analyzed to determine if it suffers from image
defects in which case it should not be selected at step 280 to reconstruct the defective face
region(s) in the main image. After this analysis and selection, if there are not enough “good” face
regions corresponding to a defective face region available from the stream of low-resolution
images, an indication is passed to the user that image repair is not viable. Where there are
enough “good" face regions, these are passed on for resizing and alignment, step 285.
This step re-sizes each face region and performs some local alignment of cardinal face points to
correct for variations in pose and to ensure that each of the low-resolution face regions overlap
one another as uniformly as is practical for later processing.
It should also be noted that as these image regions were captured in sequence and over a
relatively short duration, it is expected that they are of approximately the same size and
orientation. Thus, image alignment can achieved using cardinal face points, in particular those
relating to the eyes, mouth, lower face (chin region) which is normally delineated by a distinct
boundary edge, and the upper face which is normally delineated by a distinctive hairline
boundary. Some slight scaling and morphing of extracted face regions may be used to achieve
reasonable alignment, however a very precise alignment of these images is not desirable as it
would undermine the super-resolution techniques which enable a higher resolution image to be
determined from several low-resolution images.
It should be noted that the low-resolution images captured and stored at steps 200/210 can be
captured either from a time period before capturing the main image or from a period following
capture of the main image (indicated by the dashed line extending from step 230). For example,
it may be possible to capture suitable defect free low resolution images in a period immediately
after a subject has stopped moving/blinking etc following capture of the main image.
This set of selected defect free face regions is next passed to a super-resolution module 160
which combines them using known super-resolution methods to yield a high resolution face
region which is compatible with a corresponding region of the main acquired image.
Now the system has available to it, a high quality defect-free combination face region and a high
resolution main image with a generally corresponding defective face region.
If this has not already been performed for quality analysis, the defective face region(s) as well as
the corresponding high quality defect-free face region are subjected to AAM analysis, step 300.
Referring now to Figure 3(a) to (d), which illustrates some images including face regions which
have been processed by the AAM module 150. In this case, the model represented by the wire
frame superimposed on the face is tuned for a generally forward facing and generally upright
face, although separate models can be deployed for use with inclined faces or faces in profile.
Once the model has been applied, it returns a set of coordinates for the vertices of the wire
frame; as well as texture parameters for each of the triangular elements defined by adjacent
vertices. The relative coordinates of the vertices as well as the texture parameters can in turn
provide indicators linked to the expression and inclination of the face which can be used in
quality analysis as mentioned above.
It will therefore be seen that the AAM module 150 can also be used in the facial region analysis
steps 250/270 to provide in indicator of whether a mouth or eyes are open i.e. smiling and not
blinking; and also to help determine in steps 285/290 implemented by the super-resolution
module 160 whether facial regions are similarly aligned or inclined for selection before super-
resolution.
So, using Figure 3(a) as an example of a facial region produced by super-resolution of low
resolution images, it is observed that the set of vertices comprising the periphery of the AAM
model define a region which can be mapped on to corresponding set of peripheral vertices of
Figures 3(b) to Figure 3(d) where these images have been classified and confirmed by the user as
defective facial regions and candidates for correction.
-]0-
In relation to Figure 4, the model parameters for Figures 4(a) or 4(b) which might represent
super-resolved defect free face regions could indicate that the left-right orientation of these face
regions would not make them suitable candidates for correcting the face region of Figure 4(c).
Similarly, the face region of Figure 4(1) could be a more suitable candidate than the face region
of Figure 4(e) for correcting the face region of Figire 4(d).
In any case, if the super-resolved face region is deemed to be compatible with the defective face
region, infomiation from the super-resolved face region can be pasted onto the main image by
any suitable technique to correct the face region of the main image, step 320. The corrected
image can be viewed and depending on the nature ofthe mapping, it can be adjusted by the user,
before being finally accepted or rejected, step 330. So for example, where dithering around the
periphery of the corrected face region is used as part of the correction process, step 320, the
degree of dithering can be adjusted. Similarly, luminance levels or texture parameters in the
corrected regions can be manually adjusted by the user, or indeed any parameter of the corrected
region and the mapping process can be manually adjusted prior to final approval or rejection by
the user.
While AAM provides one approach to determine the outside boundary of a facial region, other
well-known image processing techniques such as edge detection, region growing and skin color
analysis may be used in addition or as alternatives to AAM. However, these may not have the
advantage of also being useful in analysing a face region for defects and/or for pose infomiation.
Other techniques which can prove useful include applying foreground/background separation to
either the low-resolution images or the main image prior to running face detection to reduce
overall processing time by only analysing foreground regions and particularly foreground skin
segments. Local colour segmentation applied across the boundary of a foreground/background
contour can assist in further refining the boundary of a facial region.
Once the user is satisfied with the placement of the reconstructed face region they may choose to
merge it with the main image; alternatively, if they are not happy they can cancel the
reconstruction process. These actions are typically selected through buttons on the camera user
interface where the correction module is implemented on the acquisition device 20.
As practical examples let us consider an example of the system used to correct an eye defect. An
example may be used of a defect where one eye is shut in the main image frame due to the
subject “blinking” during the acquisition. Immediately after the main image acquisition the user
is prompted to determine if they wish to correct this defect. If they confirm this, then the camera
begins by analyzing a set of face regions stored from preview images acquired immediately prior
to the main image acquisition. It is assumed that a set of, say, 20 images was saved from the one
second period immediately prior to image acquisition. As the defect was a blinking eye, the
initial testing determines that the last, say, IO of these preview images are not useful. However
the previous l0 images are determined to be suitable. Additional testing of these images might
include the determination of facial pose, eliminating images where the facial pose varies more
than 5% from the averaged pose across all previews; a determination of the size of the facial
region, eliminating images where the averaged size varies more than 25% from the averaged size
across all images. The reason the threshold is higher for the latter test is that it is easier to rescale
face regions than to correct for pose variations.
In variations of the above described embodiment, the regions that are combined may include
portions of the background region surrounding the main face region. This is particularly
important where the defect to be corrected in the main acquired image is due to face motion
during image exposure. This will lead to a face region with a poorly defined outer boundary in
the main image and the super-resolution image which is superimposed upon it typically
incorporates portions of the background for properly correcting this face motion defect. A
determination of whether to include background regions for face reconstruction can be made by
the user, or may be determined automatically after a defect analysis is performed on the main
acquired image. In the latter case, where the defect comprises blurring due to face motion, then
background regions will normally be included in the super-resolution reconstruction process. In
an alternative embodiment, a reconstructed background can be created using either (1) region
infilling techniques for a background region of relatively homogeneous colour and texture
characteristics, or (ii) directly from the preview image stream using image alignment and super-
resolution techniques. In the latter case the reconstructed background is merged into a gap in the
main image background created by the separation of foreground from background; the
reconstructed face region is next merged into the separated foreground region, specifically into
the facial region of the foreground and finally the foreground is re-integrated with the enhanced
background region.
After applying super-resolution methods to create a higher resolution face region from multiple
low-resolution preview images, some additional scaling and alignment operations are normally
involved. Furthermore, some blending, infilling and morphological operations may be used in
order to ensure a smooth transition between the newly constructed super-resolution face region
and the background of the main acquired image. This is particularly the case where the defect to
be corrected is motion of the face during image exposure. In the case of motion defects it may
also be desirable to reconstruct portions ofthe image background prior to integration of the
reconstructed face region into the main image.
It is also be desirable to match the overall luminance levels of the new face region with that of
the old face region, and this is best achieved through a matching of the skin colour between the
old region and the newly constructed one. Preview images are acquired under fixed camera
settings and can be over/under exposed. This may not be fully compensated for during the super-
resolution process and may involve additional image processing operations.
While the above described embodiments have been directed to replacing face regions within an
image, it will be seen that AAM can be used to model any type of feature of an image. So in
certain embodiments, the patches to be used for super-resolution reconstruction may be sub-
regions within a face region. For example, it may be desired to reconstruct only a segment of the
face regions, such as an eye or mouth region, rather than the entire face region. In such cases, a
determination of the precise boundary of the sub—region is of less importance as the sub—region
will be merged into a surrounding region of substantially similar colour and texture (i.e. skin
colour and texture). Thus, it is sufficient to center the eye regions to be combined or to align the
comers of the mouth regions and to rely on blending the surrounding skin coloured areas into the
main image.
In one or more of the above embodiments, separate face regions may be individually tracked (see
also lrish application S2006/0826). Regions may be tracked from frame-to—frame. Preview or
post-view face regions can be extracted, analyzed and aligned with each other and with the face
region in the main or final acquired image. In addition, in techniques according to certain
embodiments, faces may be tracked between frames in order to find and associate smaller details
between previews or post-views on the face. For example, a left eye from Joe's face in preview
N may be associated with a lefi eye from Joe's face in preview N+l. These may be used together
to form one or more enhanced quality images of Joe's eye. This is advantageous because small
features (an eye, a mouth, a nose, an eye component such as an eye lid or eye brow, or a pupil or
iris, or an ear, chin, beard, mustache, forehead, hairstyle, etc.. are not as easily traceable between
frames as larger features (and their absolute or relative positional shifts between frames tend to
be more substantial relative to their size.
The present invention is not limited to the embodiments described above herein, which may be
amended or modified without departing from the scope of the present invention as set forth in the
appended claims, and structural and functional equivalents thereof.
In methods that may be performed according to preferred embodiments herein and that may have
been described above and/or claimed below, the operations have been described in selected
typographical sequences. However, the sequences have been selected and so ordered for
typographical convenience and are not intended to imply any particular order for performing the
operations.
Claims (5)
1. An image processing method comprising: a) acquiring a main image ofa scene; b) determining one or more facial regions in said main image; c) analysing said one or more facial regions to determine if any one of said facial regions includes a defect; d) acquiring a sequence of relatively low resolution images nominally of said scene for a time period not including a time for acquiring said main image; e) determining one or more sets of low resolution facial regions in said sequence of low resolution images; i) analysing said sets of facial regions to determine if any facial regions of a set corresponding to a defective facial region of said main image include a defect; g) combining defect free facial regions of said corresponding set to provide a high quality defect free facial region; and h) correcting at least a portion of any defective facial regions of said main image with image infonnation from a corresponding high quality defect free facial region.
2. A method according to claim I wherein said time period includes one or more of a time period preceding or a time period following said time for acquiring said main image.
3. A method according to claim I wherein said correcting step comprises: applying a model comprising a plurality of vertices defining a periphery of a facial region to each high quality defect free facial region and a corresponding defective facial region; and mapping pixels of said high quality defect free facial region to said defective facial region according to the correspondence of vertices for said respective regions.
4. A method according to claim 3 wherein said model comprises an Active Appearance Model (AAM).
5. An image processing apparatus comprising: a) means for acquiring a main image of a scene; b) means for determining one or more facial regions in said main image; c) analysing said one or more facial regions to determine if any one of said facial regions includes a defect; cl) means for acquiring a sequence of relatively low resolution images nominally of said scene for a time period not including a time for acquiring said main image; e) means for detemiining one or more sets of low resolution facial regions in said sequence of low resolution images; f) means for analysing said sets of facial regions to determine if any facial regions of a set corresponding to a defective facial region of said main image include a defect; g) means for combining defect free facial regions of said corresponding set to provide a high quality defect free facial region; and h) means for correcting at least a portion of any defective facial regions of said main image with image information from a corresponding high quality defect free facial region.
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
USUNITEDSTATESOFAMERICA24/05/20071 |
Publications (2)
Publication Number | Publication Date |
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IES84961Y1 true IES84961Y1 (en) | 2008-09-03 |
IE20070518U1 IE20070518U1 (en) | 2008-09-03 |
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